Seismic Random Noise Attenuation via 2D Adaptive Filtering
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Solution Overview
Problem
Seismic data processing is hindered by random noise, which degrades the visibility and interpretability of both pre-stack and post-stack data, despite existing noise reduction methods failing to effectively remove high-amplitude random noises.
Innovation Solution
A distributed computing system employing a 2D local adaptive filter is used to decompose 3D seismic image data into sub-cubes for parallel processing, applying a filter with a variable support size based on local image statistics to attenuate random noise while preserving seismic event features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional noise reduction methods are applied to seismic data, then some noise attenuation is achieved, but high-amplitude random noises remain and data interpretability is still degraded
Solution Approach 1:
The patent applies a local adaptive filter that adjusts its parameters based on local statistics of the seismic data. The filter adapts to local variations in signal characteristics, allowing effective noise attenuation while preserving important seismic events. This local adaptation enables the filter to distinguish between random noise and coherent seismic signals more effectively than global filtering methods.
Solution Approach 2:
The filter parameters are dynamically adjusted based on the local statistical properties of the seismic data. The adaptive nature of the filter allows it to respond to changing signal characteristics throughout the 3D seismic volume, optimizing noise attenuation performance for different regions while maintaining signal integrity.
2Object-affected harmful factors
If a 3D adaptive filter is applied directly to the entire 3D seismic image, then noise attenuation is achieved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent divides the 3D seismic image into multiple 2D slices that can be processed independently and in parallel. This segmentation approach maintains the effectiveness of adaptive filtering while enabling distributed computing, thereby reducing overall processing time and computational complexity without sacrificing noise attenuation performance.
Solution Approach 2:
The patent transitions from processing the entire 3D volume at once to processing 2D slices. This dimensional reduction allows for more efficient computation while still capturing the essential 3D characteristics of the seismic data through the stacking of filtered slices. The approach balances computational feasibility with effective noise attenuation.
3Productivity
If a fixed-size filter is applied to seismic data, then processing is computationally efficient, but the filter cannot adapt to local variations in data structure
Solution Approach 1:
The patent implements an adaptive filter where the filter parameters are determined based on local statistics of the seismic data in each region. This allows the filter to adapt to local variations in signal characteristics, such as changes in amplitude, frequency, and coherence, while maintaining computational efficiency through the use of local rather than global adaptation.
Solution Approach 2:
The filter parameters are dynamically changed based on the local statistical properties of the seismic data. By adjusting parameters such as filter length and weighting coefficients according to local data characteristics, the filter achieves both adaptability to local structures and reasonable computational efficiency.
Data Source
AI summary
Seismic image processing including filtering a three-dimensional (3D) seismic image for random noise attenuation via multiple processors. The filtering includes receiving a 3D image cube of seismic image data, decomposing the 3D image cube into 3D sub-cubes for parallel computation on the multiple processors, designing and applying a two-dimensional (2D) adaptive filter for image points on 2D image slices of the 3D sub-cubes via the multiple processors to give filtered 3D sub-cubes, and summing the filtered 3D sub-cubes to give a filtered 3D image cube.